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.

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.

10-100 m 100 m 10100 m
Experiments reach here 3 bands no experiment reaches
Clear

11 opportunities match your filters

Bio-Nano Engineer

Biology, Biotechnology & Bio-Nano Systems

Nanoscale structures that have to work in contact with living material, and what that constrains.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Bio-Nano Research Intern

Biology, Biotechnology & Bio-Nano Systems

One bio-nano interface studied computationally, with what the model leaves out written down.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Bio-Nano Systems Lead

Biology, Biotechnology & Bio-Nano Systems

What this division works on, who works on it, and the rule that approvals come before work.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Bioinformatics Scientist

Biology, Biotechnology & Bio-Nano Systems

The pipelines and the statistics the division's biological conclusions rest on.

Computational Computational research L4 · Specialist Full-Time On-site — Kolkata, West Bengal, India

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 5 more

Biomaterials Engineer

Biology, Biotechnology & Bio-Nano Systems

Materials meant for biological contact: response, degradation, and the failure the material eventually has.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Biosensor Engineer

Biology, Biotechnology & Bio-Nano Systems

Sensing concepts developed to a limit of detection derived in a realistic sample matrix.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Biotechnology Intern

Biology, Biotechnology & Bio-Nano Systems

A scoped biotechnology question worked through with its biosafety and ethics context stated.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Computational Biologist

Biology, Biotechnology & Bio-Nano Systems

Biological questions answered computationally, with confounders addressed rather than mentioned.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Computational Biology Intern

Biology, Biotechnology & Bio-Nano Systems

A public biological dataset analysed end to end in a pipeline that reruns from raw data.

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

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Principal Bioengineering Scientist

Biology, Biotechnology & Bio-Nano Systems

The division's scientific direction and the hardest problems at the biology-engineering boundary.

Computational Computational research L7 · Principal Full-Time On-site — Kolkata, West Bengal, India

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 more

Senior Bio-Nano Engineer

Biology, Biotechnology & Bio-Nano Systems

Several bio-nano programmes at once, and the ethics and safety discipline across all of them.

Computational Computational research L5 · Senior Full-Time On-site — Kolkata, West Bengal, India

Scale 10^-9 m to 10^-3 m

Molecular biology to the level of explaining how a measurement in an assay relates to the underlying biology. · Has analysed a real biological dataset and stated its batch effects or confounders. · Statistics for biological data: multiple testing, effect size, and why a significant result can be meaningless. · Python or R for bioinformatics, with a reproducible pipeline. + 4 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.