What happens when a cosmologist points their tools at the dinner plate instead of the night sky? One person who has done just that is Sarah Bridle, who studied natural sciences at the University of Cambridge in the UK and then stayed on to do a PhD in cosmology, graduating in 2000. Having spent two decades mapping dark matter and building large collaborations around open data, Bridle is now chair in food, climate and society at the University of York, applying that same quantitative, physics based mindset to food.

Food manufacturing is a huge sector: in the UK, it employs 400,000 people and contributes £28bn to the economy. Physics underpins much in the field – everything from productivity, food safety and food security to health and nutrition, waste reduction and environmental impact. Bridle talks to Gleb Yakubov, a food physicist at the University of Leeds, about what inspired their switch from cosmology to food physics and tips for those looking to follow suit. Yakubov is a committee member of the food physics group of the Institute of Physics (IOP), which bridges industry and academia, raises the profile of physics in food, and represents the community to policymakers.

What attracted you to food physics?

It began when I heard that the late David MacKay, who was working at the University of Cambridge, was ill. He worked on error-correction codes, education and artificial intelligence, before pivoting to sustainability and became chief scientific adviser to the UK energy department. I started thinking hard about why we do what we do, and what would happen without him doing the fantastic work he led on energy and climate.

I began watching his talks and learning about climate change – something I hadn’t really engaged with while immersed in astrophysics. In one of his penultimate presentations, he highlighted food as having huge potential to influence climate change through diets and how we produce food. From that moment on I wanted to understand this area – it was compelling, timely and I got obsessed.

You’ve called land use the “dark matter” of climate solutions – what do you mean by this?

Most people aren’t aware that food is responsible for up to a third of global greenhouse gas emissions and, in the UK, about 70% of land is used for agriculture. Many climate solutions involve land – forests for sequestration, biofuels, solar – and people often say “we can’t spare land, we need it for food”. But the way we use land for food can be extremely inefficient, which blocks those climate solutions. That’s a huge, underappreciated lever. 

When you started working in food, what surprised you most?

As a data geek, I expected it to be all about technical solutions but what surprised me was how dominant social and cultural factors are too. You see versions of the so-called “Jevons paradox”: introduce an efficient technology and people just use more of it, cancelling the gains. Unless, that is, you pair it with social and cultural change. Food systems aren’t just complicated; they’re a complex mix of people, incentives, norms and feedback loops.

How did you shift that approach, given that people don’t always behave rationally?

I don’t think people are random; they just use different decision criteria than many models assume. For example, if you raise the price of meat to reduce consumption, some people may see it as more of a luxury and desire it more. You can get perverse responses unless you understand the social context. On top of that, my recent work looks at the potential for civil unrest and even societal collapse – uncertainties we wouldn’t have anticipated five years ago. It pushes us to combine quantitative modelling with deeper social insight.

Can you share an example where astrophysics tools didn’t work in a food context?

Early on I tried to repurpose multispectral satellite-imaging techniques to detect blackgrass in UK wheat fields. Blackgrass is a serious weed that hits yields and this felt like a neat transfer – I’d been analysing astronomical imaging, so why not fields? But it turned out to be too difficult with satellite data alone. Variables like soil moisture, rockiness and other field conditions confounded the signals. That experience nudged me away from a pure imaging approach toward broader system modelling – and, later, back to data with more contextual integration.

Tell us about your “future food calculator”?

About five years ago we started building a quantitative model of the food system to compare interventions on a common footing: on-farm practices such as fertiliser, irrigation, crop management; consumer dietary shifts; and land-use choices. We turned it into a web-based dashboard inspired by MacKay’s energy calculator. Users can move sliders, explore UK production and consumption, greenhouse gas emissions, self-sufficiency and land use, and ask, “Do these choices get us to net zero, and with what trade-offs?”

How do you handle the “people” side – demographics and messaging – in such models and outreach?

You have to meet people where they are. Older generations, on average, are more focused on health than climate. Children and younger people tend to feel invincible about health but are very concerned about climate. If you’re trying to encourage dietary change, those differences matter for messaging and for assumptions in models.

What have you learned about information versus behaviour change?

From obesity research we know that giving people information often doesn’t change behaviour. But it does increase receptiveness to policy. You couldn’t have a sugary drinks tax without enough people accepting that sugary drinks contribute to type 2 diabetes and put pressure on the National Health Service.

I think the same is true for climate and food: we need systemic changes that make sustainable choices easy by default, and public understanding helps make those changes legitimate. Reformulation and choice architecture are good examples – you can reduce beef content in a lasagne by adding pulses, or put low-impact options first in canteens. Many people won’t even notice, but outcomes improve.

While space has an intrinsic “wow” factor, how do you create that for the food industry?

For many people there’s a complete disconnect between food and how it’s produced. Experience helps more than pictures so community garden projects, for example, let people see how hard it is to grow food and how weather drives yields. That fosters respect for food, reduces waste, increases interest in the natural world and builds practical resilience.

You’ve also advised on environmental labelling – what should a good label do?

People already face a lot of labels, and shopping may not be when they’re most receptive. Studies show labels don’t shift individual purchasing as much as you might expect. But labels do change production. In pilot programmes, adding climate labels led operators to change what they offered once they saw the differences. In Canada, when authorities announced a tighter threshold for “red” sugar labels, manufacturers reformulated to avoid red. So the big impact is upstream – on what gets produced.

What’s inside the “digital twin” of UK food you’re building and what can it tell policymakers?

We take UK production and consumption, feed use and losses, and then we test interventions and see how they affect greenhouse-gas emissions and self-sufficiency. An example is soil carbon management practices such as how to reduce tillage – the mechanical manipulation of soil to prepare seedbeds – manage crop residues and control weeds. Another is dietary and consumer preference shifts, such as reducing red meat consumption. The twin helps interrogate trade-offs and unintended consequences, and assess whether a bundle of changes can hit net-zero targets while maintaining resilience.

Herbicides like glyphosate illustrate those trade-offs. How do you manage that complexity?

Exactly. No-till – an agricultural technique that plants crops without disturbing the soil through traditional ploughing or tilling – can reduce emissions but may increase reliance on herbicides if weed pressure rises. The point is to quantify those interactions at the system level and compare pathways transparently, rather than assuming any single intervention is universally better in isolation.

How do the field’s data practices compare with astrophysics – and what would you change tomorrow?

Food physics feels like astrophysics did 20–30 years ago. In astrophysics today everybody codes, collaborations are large and data and code are open. When we released Python code on GitHub for food-system models and invited contributions, basically no-one engaged because the skills weren’t there. Many models live in Excel rather than in transparent, version-controlled code.

We need standards, shared datasets, open code and co-ordinated efforts

Sarah Bridle

Even at the policy interface there are gaps: you might get a major report with spreadsheets but not the underlying code or assumptions, which we then have to reverse-engineer. With a problem as urgent as climate change, that level of opacity isn’t defensible. We need standards, shared datasets, open code and co-ordinated efforts.

What advice do you have for physicists or mathematicians who want to follow in your footsteps?

Quantitative modelling, open-source practices and Python-based tooling are highly sought after right now. Help build transparent models and shared datasets, and join or create collaborative efforts that mirror modern astrophysics consortia. Also, find your people. The best advice I got was to “adopt your own mentors”. I worked with a career coach and asked people for 30-minute calls. Many were lukewarm, but a few were transformative and became long-term collaborators. It was painful but made all the difference.

What is a concrete step someone could take now?

One is to bring open-science habits to food-system work: publish code and assumptions, contribute quantitative skills to cross-disciplinary teams, help build shared datasets and reproducible models, and translate findings into accessible tools – like interactive calculators – for policymakers and the public.

What motivates you now?

My motivation is about doing what I can to help with the crisis we’re walking into. It’s a way to channel worries about the future into practical improvements.

Looking a decade ahead, what would success look like for the UK food system?

Success would mean that UK production and consumption are aligned with net-zero targets and that land is used more efficiently so other climate solutions can scale. Through product reformulation and choice, I hope we can make low-impact diets easy by default. I also hope that self-sufficiency and resilience improve – and that open, quantitative modelling with transparent code and assumptions is embedded in policymaking.

The post From galaxies to groceries: how Sarah Bridle made the switch from cosmology to food appeared first on Physics World.

When Ryan Hamerly was a 15-year-old at Boulder High School in Colorado, he spent his summer building a Tesla coil after seeing a classroom demonstration. “I thought that was really, really cool,” he recalls. “I wanted to make one myself, and as a result I ended up studying the theory of electricity and magnetism over one summer.”

In hindsight, Hamerly realizes he didn’t use much theory to construct the coil, which became part of a school physics project. “Most of the design was based on things I had read on the Internet,” he explains, “but I did try to teach myself electricity and magnetism – and it did work in the end.”

In 2006 Hamerly’s interest in physics took him to the California Institute of Technology (Caltech) for his undergraduate studies. Keen to do graduate research, Hamerly was most fascinated by fundamental theoretical problems in quantum field theory, such as dark matter and quantum gravity. But as he started toying between different options, he had a chance encounter with Hideo Mabuchi, an applied physicist at Stanford University.

“One thing that Hideo told me that I thought was particularly fascinating,” says Hamerly, “is that quantum field theory is just a generic tool that applies in many other fields.” He therefore ended up doing a PhD in optical and quantum computing under Mabuchi’s supervision, which he completed in 2016.

Hamerly then moved to Japan, where he studied at the National Institute of Informatics in Tokyo – a time he characterizes as “sort of a postdoc, but more an excuse to travel for a year”. He did, though, get a paper out of it “so it wasn’t wasted”. In Tokyo, Hamerly worked with Mabuchi’s longtime friend Yoshihisa Yamamoto – a quantum optics researcher who had previously led a group at Japanese telecoms giant NTT for over two decades.

Step into industry

When Hamerly returned to the US in 2017, he did a formal postdoc under quantum photonics engineer Dirk Englund at the Massachusetts Institute of Technology (MIT). And in a fortuitous turn of events, Yamamoto also came to the US to establish the Silicon Valley start-up NTT Research, with the intention of forming collaborations with US universities.

“[Yamamoto] just called me up and said they had a position at NTT to work in optical computing,” he says. “At the time it made most sense to stay at MIT working as a collaborator [rather than relocate to the NTT campus] because that was where a lot of our experiments were being done.”

Today, Hamerly is a senior research scientist at NTT Research’s Physics and Informatics (PHI) Lab in Sunnyvale, California. He divides his time between MIT – where his collaboration with Englund continues – and the PHI laboratory, about 15 km from Stanford. He works at the intersection of optics, deep learning and quantum computation. For example, one of Hamerly’s projects looks into ways to reduce the energy consumption of AI data centres using optical interconnects in place of electronic ones.

Researchers hope it might one day be possible to design processors that perform calculations using optical logic that are either impossible, or at least energetically unfeasible, using electronic logic. To this end, in January 2026 Hamerly co-founded Opticore – a company producing photonic integrated circuits for energy-efficient, high-speed AI – with UC Berkeley professors Mengjie Yu and Zaijun Chen, who was previously a postdoc in Englund’s MIT group.

Different setting, same science

Hamerly does not view his place at NTT Research as purely an industry position. “The mission at NTT Research is about generating ideas that will ultimately have some use in industry,” he explains, “but the fundamental focus is on those ideas and on the research.”

The mission at NTT Research is about generating ideas that will ultimately have some use in industry

His experience at NTT Research has shown him that an industrial lab can be smaller and more focused than one at a university – but that can be a double-edged sword. “[At a university] it’s nice to be able to go into seminar rooms and see a talk from a travelling world expert in a different field,” he says, “but on the other hand, the average person [at NTT] tends to be a lot more experienced and productive than say a graduate student in academia who is still learning.”

However, the chance to meet and interact with scientists from different areas is still the same. “Science is science, regardless of what institution you’re affiliated with,” he says. “We’re still doing the same things: we do research in a lab, we write up the results, submit them and publish them in journals, and we go to conferences and present our work.”

Hamerly still sees himself as principally a theorist. He spends much of his time writing computer code – occasionally for models that run on a supercomputer but usually for models that can run on his laptop. The rest of his time is focused on writing papers, travelling to present work, responding to reviews or in meetings.

Looking to the future, Hamerly believes that technology will impact research, but that the core elements of a researcher’s role are likely to remain similar for the foreseeable future. “The job of a researcher before the Internet, for example, is not too different from a post-Internet researcher,” he says, pointing to how you still have to communicate research via papers and conferences, and understand the field by reading others’ publications.

“There will definitely be changes in the way you do every one of these things in the next 10 years, especially with AI, but I don’t see any of them becoming optional.”

For young researchers entering the field, Hamerly advises keeping your options open. He did not envisage himself leaving full-time academia and work in an industrial lab, but switched because it offered a better opportunity to pursue the research he wanted to do. “They are both good routes to do research,” he concludes, “but you might find – based on which offers you get – that one is better than another.”

The post From ideas to industry: a theoretical physicist’s journey into Silicon Valley appeared first on Physics World.

Born and raised in Egypt, Mary Bishai studied physics at the University of Colorado, Boulder, US, before doing a master’s and PhD, also in physics, at Purdue University. After a postdoc at Fermilab, she joined Brookhaven National Laboratory in 2004, where she is now a distinguished scientist. Bishai served as co-spokesperson for the Deep Underground Neutrino Experiment (DUNE) from 2023 to 2025 and, over the years, has collaborated on many other leading particle-physics projects, including MicroBooNE, the Daya Bay neutrino experiment, the Main Injector Neutrino Oscillation Search, the CDF experiment at Fermilab, and the CLEO experiment at Cornell University. In 2026 she was elected fellow of the American Association for the Advancement of Science.

What skills do you use every day in your job?

I enjoy brainstorming. If I am having a mental block or get stuck in a technical or personal interaction issue, I walk down the hall to chat with a colleague about their work – even if it isn’t related to my own problem. This serves two purposes. First, it helps ameliorate the feelings of loneliness and frustration to discuss your challenges with others. Second, a surefire cure for any mental block is to divert your attention to a different topic. Another essential skill is to try to play devil’s advocate with myself and colleagues. My team finds this infuriating sometimes, but it’s vital – you need to be your toughest critic.

What do you like best and least about your job?

For a self-professed nerd like me, doing big science is heaven. I get to geek out on a broad set of technical and scientific challenges and work with lots of really cool experts from a variety of disciplines. Recently, for example, as part of DUNE, I’ve been looking at designs for the infrastructure that houses the Long Baseline Neutrino Facility. Located at Fermilab near Chicago, it will send the world’s most powerful beam of neutrinos to a detector in an underground lab 1300 km away in South Dakota.

In any given week, I might find myself discussing the mechanical engineering of detectors or learning about the civil engineering required for large-scale facilities. I might be discovering fascinating details about the hydrogeology of the Fermilab site, exploring the design of focusing magnets, or studying the results of simulations of high-energy particle interactions in materials.

I could also find myself in a meeting of the artificial intelligence (AI) expert team, learning new AI approaches, such as improving the search for neutrinos from supernovae using our detectors. Wandering the hallways, I might bump into a colleague from Brookhaven’s chemistry division and get chatting about new detector materials. I also work with theorists on new ideas for physics at DUNE.

I’m happiest trying to connect the dots between physics goals and technical and engineering disciplines, coming up with multidisciplinary design solutions to realize these massive science projects

I trained as an experimental particle physicist, but I’m happiest trying to connect the dots between physics goals and technical and engineering disciplines, coming up with multidisciplinary design solutions to realize these massive science projects. Best of all is when I ask a question in an area I’m not an expert in – say civil engineering – and someone replies: “Hmm, we hadn’t thought about that.”

My least favourite part of the job is that it can be very challenging to deal with large international collaborations of scientists and engineers over the very long time scales of large-scale projects. You need a lot of emotional resilience, stamina and patience to keep going for the decades it takes to get these large-scale projects to fruition. Burn out and despondency are downsides; keeping yourself and your team going is very difficult.  

What do you know today that you wish you knew when you were starting out in your career?

Always be honest to the science. If it looks like something you put a lot of effort into is a dead end, drop it; don’t wait until someone tells you to stop. There will be a lot of dead ends before you find the right direction. When an approach fails, it means you have a new intellectual challenge – and that’s the fun part. Don’t be afraid to challenge established dogma and groupthink, even if it means difficult discussions with your colleagues no matter how senior. And most importantly, don’t keep pushing when you are burning out. It’s okay to step back and slow down for a while.

The post Ask me anything: Mary Bishai – ‘For a self-professed nerd like me, doing big science is heaven’ appeared first on Physics World.

For Keamogetswe Ramonaheng, physics was never just about equations – it was about clarity. “From a young age, I was attracted to mathematics and science as a way of understanding complex phenomena through a structured approach,” she says. “Physics was the area that spoke to me the most because it is the foundation for the fundamental principles that govern the natural world.”

Ramonaheng is head of medical physics and radiobiology at the Nuclear Medicine Research Infrastructure (NuMeRI) in Pretoria, South Africa, where she applies the principles of radiation science to treat cancer. NuMeRI, which opened in 2024, is the first research facility in Africa dedicated to nuclear medicine. It’s a joint venture between the Steve Biko Academic Hospital, the University of Pretoria, iThemba Laboratories for Accelerator-Based Sciences and the Nuclear Energy Corporation of South Africa.

Ramonaheng’s academic journey began at the University of the Free State (UFS), where she completed her undergraduate and honours studies before starting an internship at Universitas Academic Hospital in Bloemfontein. There she saw how a rigorous physics training can lead to tangible, clinical benefits. “The ability to comprehend and harness the interaction between radiation and matter in the human body demonstrated the power and relevance of scientific inquiry,” she recalls.

In many ways, nuclear medicine found me

Keamogetswe Ramonaheng

Thanks to a fellowship from the International Atomic Energy Agency (IAEA), Ramonaheng completed a clinical placement at Royal North Shore Hospital in Sydney, Australia. She later continued her postgraduate studies at UFS, becoming the first Black South African woman to earn a PhD in medical physics for nuclear medicine. “In many ways, nuclear medicine found me,” says Ramonaheng, who is grateful to the encouragement of various senior staff members who saw her potential and guided her into the field.

Multifaceted role

Following a spell as an independent medical physicist and manager at Universitas Academic Hospital and lecturer at UFS, Ramonaheng joined NuMeRI in 2024 and the University of Pretoria. Along with the team of scientists she leads, Ramonaheng oversees the safe and effective use of ionizing radiation at NuMeRI used to treat and diagnose disease in a safe and effective manner.

It’s a varied role, which stretches from providing patient-focused clinical services to carrying out applied research. “We integrate research with operations,” says Ramonaheng. “That requires careful planning and rigorous quality assurance, ensuring that innovation does not compromise safety.”

Among her duties, Ramonaheng carries out dosimetry calculations for innovative radiopharmaceuticals, works on new forms of quantitative imaging, and helps to develop novel radionuclide therapies, including using alpha particles to treat cancer. She also uses gamma-ray cameras equipped with highly sensitive cadmium-zinc-telluride detectors, which allow radiopharmaceuticals to be quantified and imaged more precisely.

Ramonaheng is particularly interested in “theranostics” – a form of “precision medicine” that combines therapy with diagnostics. It involves giving a patient a tumour-targeting molecule labelled with a radionuclide. This allows the tumour to be visualized using techniques such as positron emission tomography (PET) or single-photon emission computerized tomography (SPECT). The same molecule – or one similar to it – is then used to deliver a therapeutic radionuclide directly to the tumour.

Daily challenges

For Ramonaheng, a typical day is fast-paced. Mornings often begin with her overseeing radiation-safety protocols and ensuring that radiation imaging and counting equipment are working as well as possible, such that they meet quality assurance standards. Through the day, Ramonaheng also oversees all operational medical-physics activities and carries out her duties as chair of NuMeRI’s radiation protection committee.

As the day progresses, she might find herself reviewing clinical theranostics dosimetry workflows to carrying out patient-specific dose calculations or evaluating quantitative imaging metrics from SPECT/CT and PET/CT systems. Other tasks include reviewing research protocols for cancer theranostics, mentoring postgraduate students at the University of Pretoria, and examining clinical trials.

Innovation accelerates when silos are dismantled

Keamogetswe Ramonaheng

Ramonaheng works in a highly interdisciplinary environment, collaborating with radiographers, nurses, radiochemists, radiopharmacists, medical physicists and clinicians to address live issues in real time. “Innovation accelerates when silos are dismantled,” she says.

The work is not without its challenges. Funding for postgraduate training is a persistent concern. Clinical physics is also a highly specialized field, which means it can be hard to recruit people with the right skills, who might be drawn to better-paid industry jobs. In addition, NuMeRI is an operationally complex mix of advanced imaging systems, radiopharmaceuticals and clinical regulations, which requires good project-management and planning skills.

But Ramonaheng, who recently won two awards at the 8th Theranostics World Congress in Cape Town, feels the benefits outweigh the challenges. “It is very fulfilling to see the translation of research into clinical application,” she says. Just as gratifying, she adds, is watching her students move from their studies to publications and clinical applications. “You see the entire process of scientific advancement.”

A more promising future

Looking ahead, Ramonaheng envisages a growing use of artificial intelligence (AI) in her work. She also collaborates with national and international partners to automate workflows and enhance efficiency, precision and patient-centred care. Another ambition for Ramonaheng is to further strengthen NuMeRI as an Africa-wide hub for research, clinical service and training – a vision reinforced by the IAEA recently naming NuMeRI as one of 18 global “anchor centres” for its work in radiotherapy and medical imaging.

Ramonaheng believes medical physics will grow rapidly in Africa over the next 10 years, fuelled by an expansion of theranostics and precision medicine. Her hope is to guide this growth through mentorship and leadership, ensuring that Africa develops its own talent pool of medical physicists who can address the continent’s unique healthcare needs.

Africa suffers, for example, from limited access to advanced imaging and targeted therapies. Ramonaheng’s aim is to optimize personalized and precision medicine for cancer patients, ultimately improving treatment outcomes and quality of life. Eventually, she hopes, medical physics will be recognized as a profession across the continent. “We are building not only research outputs but human capital.”

Leadership is not only about the creation of paths, but the creation of paths where there were no paths previously

Keamogetswe Ramonaheng

Being a pioneer in the field has required resilience on her part. “Competence must be coupled with confidence,” says Ramonaheng, who has had to learn the unwritten rules of a world dominated by men. As a mentor, her guiding principle is the African concept of motho ke motho ka batho babang – a person is a person only through others. “Leadership is not only about the creation of paths,” she says, “but the creation of paths where there were no paths previously.”

Her message to young physicists – particularly women and those from other underrepresented groups – is clear. “Medical physics is a dynamic and impactful field at the intersection of physics, medicine and technology,” she says. “ It allows you to see the direct translation of science to patients.” Medical physics requires resilience, curiosity and commitment, but for Ramonaheng its beauty is that equations don’t stay on paper – they become a tool for healing.

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Artificial intelligence may not deliver on its promise of vast economic opportunity at a price that humanity is willing to pay

In March, Anthropic, the cutting-edge artificial intelligence business that gave us the chatbot Claude, published an analysis on the impact of AI on employment, to help us assess the claim that intelligent robots were about to redefine human existence, ending demand for human labor.

Last year in May, Anthropic’s co-founder, Dario Amodei, claimed AI could wipe out half of all entry-level jobs in one to five years. Last January, he told us AI would probably become a “general labor substitute for humans”. In June he said we risk “a world where the economic trade-off dial is stuck on the hypergrowth, hyper-inequality setting”.