Astrophysics Research Scientist

EduRankAI; Kolkata District; In loco; INR 800.000 - 1.400.000; Tempo pieno

Descrizione del lavoro

EduRankAI seeks a researcher to model astrophysical systems and compare results with observations, spanning scales from 10^13 m to beyond observable limits. You will develop cosmological models, run simulations, and rigorously quantify uncertainties, including systematic components, while clearly separating evidence, inference, and speculation in every result.

The role emphasizes end-to-end ownership: framing the problem, performing the work, and delivering results with explicit limits.

Competenze

• 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.

Mansioni

• 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.
• Own a whole problem end to end: framing it, doing the work, and reporting the result with its limits.
• Review a colleague's work when asked, and say plainly when a result is not supported by its method.

Conoscenze

Redshift explanation

Astronomical data analysis

Statistics & Bayesian inference

Python for data analysis

C++

Julia

Fortran

Strumenti

Python

C++

Julia

Fortran

Descrizione del lavoro

Astrophysical systems modelled and compared against observation, at scales that are observed.

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: Astrophysical systems modelled and compared against observation, at scales that are observed. Scale range for this work: 10^13 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. 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 3 (Engineer and Scientist). A whole problem end to end: framing it, doing the work, and reporting the result with its limits.

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.
• Own a whole problem end to end: framing it, doing the work, and reporting the result with its limits.
• Review a colleague's work when asked, and say plainly when a result is not supported by its method.

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