Bilimsel Hesaplama Jobs

Project Overview We are developing an internal AI-driven GC-MS analytical modeling system for flavor formulation reverse engineering. This is not: A chatbot project A basic automation workflow A generic machine learning classification task A web dashboard build This is a scientific inverse modeling and constrained optimization system built around GC-MS analytical data. If you do not have experience with scientific modeling, regression, and constrained optimization, please do not apply. System Objectives We are building a modular Python system that: Parses structured GC-MS peak lists (CSV/Excel, sometimes PDF reports). Performs CAS-based compound normalization and matching. Handles retention time alignment. Applies weighted similarity scoring with marker compound prioritizatio...

Python Developer – GC-MS Data Analysis & Automated Flavor Recipe Generation System Project Overview We are a flavor manufacturing company developing an internal AI-driven GC-MS automation system. The goal of this project is to: Process GC-MS peak data Match peaks with compound libraries Identify possible natural/synthetic sources Detect marker compounds Estimate ingredient combinations Automatically generate optimized trial recipes (normalized to 100%) This is NOT a simple chatbot or automation project. This is a scientific data processing and algorithm development project. Responsibilities Parse GC-MS peak lists (CSV / Excel exports) Implement compound matching logic (CAS-based matching) Normalize peak area percentages Develop similarity scoring algorithms Ident...

I have a time-sensitive batch of NMR, mass spectrometry and IR spectra that must be interpreted accurately. Each sample contains several unknowns; I need both a confident structural identification for every component and a reliable quantitative breakdown of their relative abundances. The raw data have already been exported in common formats, so you can work in MestReNova, TopSpin, MassLynx, OpenChrom, MATLAB, Python (e.g., nmrglue, pymzML) or any comparable toolset you prefer, provided the final results are fully traceable back to the original spectra. Turnaround is critical: an initial draft report within 48 hours, with a short feedback cycle, then finalised results no later than five days from acceptance. Please show specific experience handling combined NMR–MS–IR workflow...