Departments / Extreme-Scale, Nano & Fundamental Engineering / D08

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.

Applied engineering Established science 10^-100 m to 10^100 m 15 open

What this work is, and what it is not

A model that fits is not a model that predicts. Work from this division reports a held-out result and a competent classical or numerical baseline alongside every machine-learning claim, and states the domain a surrogate is valid over. A digital twin is a model of a system, not the system, and is described that way.

Established science. Rests on settled physics and engineering. The work applies it; it is not what is under test here.

Part of this division's range sits where no experiment reaches: Ultra-fundamental and theoretical, Planck, fundamental and high-energy theory, Cosmological, theoretical and computational. Work in those bands is theory and computation, and every posting says which side of that line it falls on.

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.

Where this division sits

Scale range 10^-100 m to 10^100 m

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10^-100 m 10⁰ m 10¹⁰⁰ m

Experiments reach here 3 bands no experiment reaches This role

B01

Ultra-fundamental and theoretical (10^-100 to 10^-35)
Below the Planck length. No experiment reaches here and none is proposed; work in this band is mathematics and theory about what a physical theory would have to look like at all.

B02

Planck, fundamental and high-energy theory (10^-35 to 10^-18)
From the Planck length up to the smallest distances probed by collider experiments. Almost all of this band is inferred from theory rather than measured.

B03

Subatomic, particle and nuclear (10^-18 to 10^-12)
Quarks, nucleons and nuclei. Studied through accelerators, detectors, and the statistical analysis of very large datasets.

B04

Atomic and quantum (10^-12 to 10^-9)
Atoms, ions, electronic structure and quantum states. Directly measured with spectroscopy, traps and quantum devices.

B05

Nano (10^-9 to 10^-6)
Molecules, nanostructures, two-dimensional materials and quantum dots. Fabricated, imaged and characterised routinely.

B06

Micro (10^-6 to 10^-3)
Microelectronics, MEMS, microfluidics and cellular biology. Mature fabrication and metrology.

B07

Meso and human-scale devices (10^-3 to 10^0)
Components, instruments and devices a person can hold. Where most engineering prototypes are built and tested.

B08

Macro, infrastructure and planetary surface (10^0 to 10^6)
Machines, structures, vehicles, buildings and infrastructure networks, up to regional scale.

B09

Space, planetary and astronomical (10^6 to 10^13)
Planetary bodies, orbits and the Solar System. Reached by spacecraft and observed directly.

B10

Stellar and galactic modelling (10^13 to 10^26)
Stars, star systems, galaxies and large-scale structure. Observed remotely and modelled computationally. Nothing at this scale is engineered.

B11

Cosmological, theoretical and computational (10^26 to 10^100)
At and beyond the edge of the observable universe. A statement about this band is a statement about a cosmological model, not about anything that can be observed.

Charter

This division exists to serve all the others. It builds the research software, makes simulations run at the sizes the science needs, develops AI models for scientific problems with honest baselines, and holds the line on reproducibility and code quality across the whole department.

Domains

Scientific computingResearch software engineeringHigh-performance computingSimulation platformsAI for sciencePhysics-informed machine learningDigital twinsScientific data pipelinesReproducible computing

Disciplines

Computer science Scientific computing Artificial intelligence Mathematics

Works with

Divisions this one collaborates with routinely.

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15 open opportunities

Grouped by career rung. Every posting states its own classification, its scale range and what it expects you to have already done.

Level 1 4

AI for Science Intern

A scientific prediction task with a proper held-out split and a classical baseline reported beside the model.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

HPC Intern

Parallelising one real workload and reporting the measured speed-up and where it stopped scaling.

Applied engineering Established science L1 · Research and Engineering Intern Internship On-site / Hybrid — Kolkata, West Bengal, India

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Research Software Intern

Taking a working research script and turning it into a tested, documented package somebody else can install.

Applied engineering Established science L1 · Research and Engineering Intern Internship On-site / Hybrid — Kolkata, West Bengal, India

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Scientific Computing Intern

One numerical method implemented, tested against an analytic case, and profiled.

Applied engineering Established science L1 · Research and Engineering Intern Internship On-site / Hybrid — Kolkata, West Bengal, India

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Level 3 5

Computational Engineer

The computation another division needs, made correct, fast enough, and reproducible.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

HPC Engineer

Making the department's workloads run at scale, with measured scaling curves rather than claims.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 9 more

Research ML Engineer

Machine learning on scientific data, with the evaluation designed before the model is chosen.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 8 more

Scientific Software Engineer

Research software built to survive handover: tested, documented, and maintained.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Simulation Engineer

Simulation platforms: solvers, verification, and the convergence evidence behind every run.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Level 4 3

AI for Materials Engineer

Property prediction and screening for the materials divisions, with false-positive rates reported.

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

Scale 10^-10 m to 10^-6 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Digital Twin Engineer

Twins of real systems, with a written statement of what each one represents and where it stops being valid.

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

Scale 10^-3 m to 10^6 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Physics-Informed AI Engineer

Models that carry the physics as a constraint, and the honest account of when that helps and when it does not.

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

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 7 more

Level 5 1

Senior Scientific Computing Engineer

Several divisions' computational needs at once, and the engineering standard applied across them.

Applied engineering Established science L5 · Senior Full-Time On-site — Kolkata, West Bengal, India

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Level 6 1

Scientific AI Lead

What this division builds for the others, who builds it, and the rule that no model ships without its baseline.

Applied engineering Established science L6 · Lead Full-Time On-site — Kolkata, West Bengal, India

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Level 7 1

Principal AI for Science Engineer

How the department applies machine learning to science, and the evidence standard that applies to all of it.

Applied engineering Established science L7 · Principal Full-Time On-site — Kolkata, West Bengal, India

Scale 10^-100 m to 10^100 m

Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more

Other divisions in this department

Ultra-Fundamental & Mathematical Physics Particle, High-Energy & Nuclear Systems Quantum, Atomic & Precision Systems Molecular Engineering & Computational Chemistry Nano Engineering & Nanotechnology Materials & Advanced Matter Semiconductors, Electronics & Micro/Nano Systems Mathematics & Computational Foundations Mechanical, Robotics & Multi-Scale Systems Civil, Structural & Macro-Scale Systems Biology, Biotechnology & Bio-Nano Systems Energy, Environment & Climate Materials Aerospace, Space & Extreme Environment Engineering Astrophysics, Cosmology & Extreme-Scale Modelling