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Careers · Research opportunities

Search by what the work actually is

Every research posting carries a classification and a scale range. Filter by either, and by division, discipline, career rung or how recently it was posted. Everything you choose is kept in the address bar, so a search can be shared or bookmarked.

Scale bands are a research classification, not a claim of experimental reach. Most of this span cannot be probed by any apparatus that exists: nothing below about 10^-19 m has been measured directly, and anything at 10^26 m or beyond is inferred from observation rather than engineered. Every opportunity states the kind of work it actually is.

All roles across every team → Department directory →

Browse by scale band

Eleven bands from 10-100 m to 10100 m. Click one to filter. The hatched bands are the ones no apparatus reaches.

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Experiments reach here 3 bands no experiment reaches

1197 opportunities

Page 30 of 50

Aerospace Engineer

Aerospace, Space & Extreme Environment Engineering

Aerospace systems analysed and designed against requirements with traceable margins.

Applied engineering Established science L3 · Engineer and Scientist Full-Time On-site — Kolkata, West Bengal, India

Scale 10^3 m to 10^13 m

Orbital mechanics: can propagate a two-body orbit and explain what perturbs it. · Has run an engineering analysis for a system with mass, power and thermal constraints that trade against each other. · Understands the space environment: vacuum, radiation, thermal cycling, and what each does to hardware. · Python for mission and systems analysis. + 4 more

Astrophysics Data Engineer

Astrophysics, Cosmology & Extreme-Scale Modelling

The pipelines survey data flows through, and the provenance that lets a figure be traced to a run.

Computational Computational research L3 · Engineer and Scientist Full-Time On-site — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 7 more

Astrophysics Research Intern

Astrophysics, Cosmology & Extreme-Scale Modelling

One astrophysical question answered from public survey data, selection function included.

Computational Computational research L1 · Research and Engineering Intern Internship On-site / Hybrid — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more

Astrophysics Research Scientist

Astrophysics, Cosmology & Extreme-Scale Modelling

Astrophysical systems modelled and compared against observation, at scales that are observed.

Computational Computational research L3 · Engineer and Scientist Full-Time On-site — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more

Computational Astrophysics Intern

Astrophysics, Cosmology & Extreme-Scale Modelling

A small simulation run at two resolutions, with the convergence difference reported.

Simulation Computational research L1 · Research and Engineering Intern Internship On-site / Hybrid — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more

Computational Cosmologist

Astrophysics, Cosmology & Extreme-Scale Modelling

Cosmological simulation and inference, with convergence and systematics treated as first-class.

Computational Computational research L3 · Engineer and Scientist Full-Time On-site — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more

Cosmology & Extreme-Scale Lead

Astrophysics, Cosmology & Extreme-Scale Modelling

What this division works on, who works on it, and the rule that beyond the observable horizon we describe a model, not the universe.

Computational Computational research L6 · Lead Full-Time On-site — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more

Cosmology Intern

Astrophysics, Cosmology & Extreme-Scale Modelling

A cosmological calculation carried out and compared against published constraints.

Theoretical Theoretical research L1 · Research and Engineering Intern Internship On-site / Hybrid — Kolkata, West Bengal, India

Scale 10^13 m to 10^100 m

Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more

Browse by division

Each division states the scale it works at and the kind of work it does, at the top of its own page.

Ultra-Fundamental & Mathematical Physics

Mathematical and theoretical work on the structure of spacetime, quantum foundations and the frameworks that attempt to describe physics below the Planck length. Entirely theory, mathematics and computation.

Particle, High-Energy & Nuclear Systems

Modelling and data analysis for particle, high-energy and nuclear physics: interaction models, Monte Carlo pipelines, detector data, and radiation effects in materials.

Quantum, Atomic & Precision Systems

Quantum information, algorithms, device modelling, sensing and atomic simulation, in the band where quantum states are measured directly rather than inferred.

Molecular Engineering & Computational Chemistry

Quantum chemistry, molecular dynamics and molecular design: computing what a molecule does, and comparing the prediction with measurement wherever measurement exists.

Nano Engineering & Nanotechnology

Nanomaterials, nanostructures, nanoelectronics, nanophotonics, sensing and metrology, across the band where structures are fabricated, imaged and characterised routinely.

Materials & Advanced Matter

Materials discovery and modelling across alloys, ceramics, polymers, composites, metamaterials, quantum and energy materials, and materials for extreme environments.

Semiconductors, Electronics & Micro/Nano Systems

Device physics, microelectronics and VLSI, MEMS and NEMS, sensors and photonics, in the band where design, simulation and prototype meet.

Computer Science, AI & Scientific Computing

The cross-department division: research software, simulation platforms, HPC, AI for science, digital twins, data pipelines and the reproducibility every other division depends on.

Mathematics & Computational Foundations

The mathematical foundations the whole department stands on: numerical methods, convergence and stability, optimisation, probability, and the modelling support every other division draws on.

Mechanical, Robotics & Multi-Scale Systems

Computational mechanics, robotics, precision engineering and advanced manufacturing, and the multiscale modelling that carries a material property up into a system that has to work.

Civil, Structural & Macro-Scale Systems

Structural engineering, smart infrastructure, advanced construction materials, infrastructure sensing and digital twins, and climate-resilient large-scale systems.

Biology, Biotechnology & Bio-Nano Systems

Nanobiotechnology, biomaterials, biosensors, computational biology and bioinformatics, tissue engineering, lab-on-chip and microfluidics, and the interfaces between biology and engineered surfaces.

Energy, Environment & Climate Materials

Battery and energy-storage materials, hydrogen systems, solar materials, catalysis, carbon capture, water purification, environmental nanotechnology and climate systems modelling.

Aerospace, Space & Extreme Environment Engineering

Aerospace and space systems engineering, extreme-environment materials, orbital and planetary systems modelling, space robotics and the architecture work behind long-horizon space research.

Astrophysics, Cosmology & Extreme-Scale Modelling

Computational astrophysics and cosmology: observational data analysis, gravitational and structure modelling, large-scale simulation, and the mathematics of models at and beyond the observable horizon.