Senior Computational Astrophysicist

EduRankAI; Kolkata District; In loco; INR 800.000 - 1.500.000; Tempo pieno

Descrizione del lavoro

EduRankAI seeks a Senior Analyst in cosmology to work on multiple simulation and analysis programmes, ensuring convergence and rigorous uncertainty quantification. You will develop cosmological models, run scale-appropriate simulations, and clearly separate evidence from inference in every result.

The role requires Python for astronomical data analysis, strong Bayesian skills, and experience with large observational datasets and model validation.

Competenze

• Explain what a redshift measures and what assumptions convert it to distance.
• Analyse a real astronomical dataset, including its selection function.
• Use statistics and Bayesian inference: priors, posteriors, and credible intervals.

Mansioni

• Analyse observational and public datasets, with the selection function addressed.
• Develop cosmological and astrophysical models and state assumptions.
• Run simulations with resolution convergence tested.
• Quantify uncertainty, including dominant systematic uncertainty at this scale.
• Compare models against observations and report poor fits.

Conoscenze

Redshift concepts

Astronomical datasets

Bayesian statistics

Python for astronomy

Simulation labeling

Python

C++

Julia

Fortran

Strumenti

Python

C++

Julia

Fortran

Descrizione del lavoro

Several simulation and analysis programmes at once, and the convergence discipline across them.

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: Several simulation and analysis programmes at once, and the convergence discipline across them. 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 5 (Senior). Several related problems at once, and the technical judgement of less experienced colleagues.

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.
• Carry several related problems at once without losing the thread of any of them.
• Raise the technical judgement of less experienced colleagues, in review and in person.

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