Search by what the work actually is

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Computer Science, AI & Scientific Computing

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

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

Materials & Advanced Matter

Two-dimensional materials, where the substrate and the edges determine most of what is measured.

Scale 10^-10 m to 10^-7 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

A scoped review of one advanced-material class, separating measured properties from computed ones.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

What this division searches for, who searches, and the rule that a prediction is labelled a prediction.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

The calculations: which level of theory, how convergence was tested, what the number is worth.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

Turning characterisation runs into defensible property values with uncertainty.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 7 more

Materials & Advanced Matter

Materials selected and specified against an engineering requirement another division has stated.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

One structure-property relationship investigated computationally and checked against measured data.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

The division's technical direction, and the hardest problems between a computed candidate and a usable material.

Scale 10^-10 m to 10^0 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more

Materials & Advanced Matter

Materials whose useful behaviour is quantum, and the calculations and measurements that establish it.

Scale 10^-10 m to 10^-8 m

Can relate a crystal structure to at least one measurable property and explain the mechanism. · Reads a phase diagram and can say what happens on cooling through a boundary. · Has analysed real characterisation data — diffraction, microscopy or mechanical testing — and stated its uncertainty. · Python for data analysis over a materials dataset. + 4 more