Computational Astrophysics Intern

EduRankAI; Kolkata District; Presencial; INR 201.000 - 312.000; A tiempo parcial

Descripción de la vacante

EduRankAI in India invites applications for an internship in astrophysics simulations. Work within the Department of Extreme-Scale, Nano & Fundamental Engineering on cosmological models, data analysis, and scale simulations.

You will analyze public and observational datasets, test convergence, and quantify uncertainty under supervision, delivering a concise written report. Ideal candidates possess Python and C++/Julia/Fortran skills, familiarity with Bayesian inference, and experience handling

Formación

• Explain what redshift measures and how to convert it into distance.
• 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.
• Label simulation output as simulation in every figure, without being asked.
• Python
• C++
• Julia
• Fortran

Responsabilidades

• Analyse observational and public datasets, with the selection function addressed.
• Develop cosmological and astrophysical models and state what each one assumes.
• Run simulations at the scale the question requires, with resolution convergence tested.
• Quantify uncertainty, including the systematic uncertainty that dominates at this scale.
• Compare models against observations and report the comparisons that went badly.
• Separate evidence, inference and speculation explicitly in every written result.
• 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.

Conocimientos

Python

C++

Julia

Fortran

Statistics

Bayesian inference

Descripción del empleo

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

Department of Extreme-Scale, Nano & Fundamental Engineering, Division D15: Astrophysics, Cosmology & Extreme-Scale Modelling. This division analyses observational and public datasets, develops cosmological models, runs simulations, quantifies uncertainty, and compares models against observations. It distinguishes evidence, inference and speculation in every result, because at this scale those three are easy to confuse and expensive to confuse. This position: A small simulation run at two resolutions, with the convergence difference reported. Scale range for this work: 10^13 m to 10^100 m. Research classification: Simulation. Building and running simulations of systems too large or too strongly coupled to solve directly. 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. Nothing at this scale is engineered and nothing is experimented on. The observable universe has a radius of roughly 10^26 m; everything beyond that is a property of a cosmological model rather than an observation, and this division says so every time it publishes a figure that extends past it. Simulation output is a model realisation, not data, and is labelled as such in every figure it appears in. 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

• Analyse observational and public datasets, with the selection function addressed rather than ignored.
• Develop cosmological and astrophysical models and state what each one assumes.
• Run simulations at the scale the question requires, with resolution convergence tested.
• Quantify uncertainty, including the systematic uncertainty that dominates at this scale.
• Compare models against observations and report the comparisons that went badly.
• Separate evidence, inference and speculation explicitly in every written result.
• 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

• 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.
• Labels simulation output as simulation, in every figure, without being asked.
• Python
• C++
• Julia
• Fortran