AI for Science Intern

EduRankAI; Kolkata District; Presencial; INR 223.000 - 446.000; Jornada completa

Descripción de la vacante

EduRankAI is seeking a Level 1 Research and Engineering Intern to contribute to scientific prediction tasks, data pipelines, and reproducible results within the Division D08: Computer Science, AI & Scientific Computing. You will help build research software and scalable simulations to support diverse divisions.

The role emphasizes delivering a reproducible artefact, reporting baselines, and collaboration with a named supervisor on a weekly basis.

Formación

• Fluent in Python and able to write readable, testable code.
• Data structures and algorithms at a solid level with clear justification.
• Comfortable on Linux and with Git as a working tool.
• Has written tests for numerical code and can explain tolerance in tests.
• Numerical methods: can state the error behaviour of a scheme implemented.
• Reports a baseline alongside every model result.
• Python
• C++
• Julia
• Bash

Responsabilidades

• Build research software that other divisions depend on, with reproducible artefacts.
• Build and maintain simulation platforms to scale with science needs.
• Optimise scientific workloads with measured before-and-after results.
• Develop AI models for scientific problems with baselines for comparison.
• Build digital twins and clearly state what they represent.
• Create data pipelines with provenance tracing results to runs.
• Maintain reproducibility with seeds, environments, and consistent results.
• Ensure code quality and testing across divisions' repositories.
• Work on a clearly scoped problem and deliver a written result at the end.
• Meet a supervisor weekly and discuss what didn’t work as well as what did.

Conocimientos

Python

Data structures & algorithms

Linux

Git

Testing numerical code

Numerical methods

Baseline reporting

C++

Julia

Bash

Herramientas

Python

C++

Julia

Bash

Linux

Descripción del empleo

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

Department of Extreme-Scale, Nano & Fundamental Engineering, Division D08: Computer Science, AI & Scientific Computing. 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. This position: A scientific prediction task with a proper held-out split and a classical baseline reported beside the model. Scale range for this work: 10^-100 m to 10^100 m. Research classification: Computational. Computing quantities that cannot be obtained in closed form, from established physics. Evidential standing: Computational research. Computed from established physics. A computed number is a prediction, and it is only as good as its convergence, its error budget, and whatever data it can be checked against. 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. 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. Level 1 (Research and Engineering Intern). One well-defined problem, delivered with a reproducible artefact and a short written report.

Responsibilities

• Build research software that other divisions depend on, to a standard that survives being handed over.
• Build and maintain simulation platforms, and make them run at the size the science actually needs.
• Optimise scientific workloads with a measured before-and-after, never an asserted one.
• Develop AI models for scientific problems, always reported against a competent baseline.
• Build digital twins, and state plainly what each one does and does not represent.
• Create data pipelines with provenance, so a number can be traced to the run that produced it.
• Hold the reproducibility standard: seeds, environments, and a result that comes back the same.
• Maintain code quality and testing across the department, including in other divisions' repositories.
• Work on one clearly scoped problem for the duration, with a written result at the end rather than a status update.
• Meet a named supervisor weekly and come to that meeting with what did not work as well as what did.

Skills

• 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.
• Numerical methods: can state the error behaviour of a scheme they have implemented.
• Reports a baseline alongside every model result.
• Python
• C++
• Julia
• Bash