Astrophysics Data Engineer

EduRankAI; Kolkata District; Presencial; INR 900.000 - 1.400.000; Jornada completa

Descripción de la vacante

EduRankAI seeks a data-focused scientist to analyze astronomical datasets and develop cosmological models. You will run simulations, quantify uncertainties, and compare results with observations, ensuring clear separation of evidence, inference, and speculation.

Collaboration across teams will be essential to deliver end-to-end analyses with well-communicated limitations. The role requires experience with astronomical data pipelines, statistical methods, and proficiency in Python; knowledge of

Formación

• Experience analyzing astronomical datasets.
• Knowledge of selection functions in data.
• Proficiency in Bayesian statistics and inference methods.
• Experience with Python in astronomical data analysis pipelines.

Responsabilidades

• Analyze observational and public datasets with a defined selection function.
• Develop cosmological and astrophysical models and state their assumptions.
• Run simulations with appropriate resolution and convergence checks.
• Quantify and report uncertainties, including dominant systematic errors.
• Compare models against observations and report where results diverge.
• Explicitly separate evidence, inference and speculation in written results.
• Own end-to-end problem framing, execution and reporting with clear limitations.
• Review colleagues' work and assess whether conclusions are method-supported.

Conocimientos

Python

C++

Julia

Fortran

Databases

Statistics and Bayesian inference

Astronomical data pipelines

Redshift explanation

Descripción del empleo

The pipelines survey data flows through, and the provenance that lets a figure be traced to a run.

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: The pipelines survey data flows through, and the provenance that lets a figure be traced to a run. 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.
• Astronomical data pipelines
• Databases
• Python
• C++
• Julia
• Fortran