Computational Chemistry Lead – Molecular Identification
Vor 3 Tagen
Computational Chemistry Lead – Molecular Identification At Isospec, we’re building a new standard for molecular identification to unlock patient-centric drug development and diagnostics. You’ll own the computational workflows that turn candidate molecules into high-quality IR spectra—fast, reproducibly, and at scale—enabling direct comparison to experimental data and confident molecular ID. Your Impact Build and operate scalable pipelines for in-silico IR spectra generation and experimental validation. Within 6–12 months, success means having an automated and validated pipeline running at scale for in-silico IR spectra generation. Standardized workflows for predicting and comparing spectra should be consistently applied across projects, enabling fast turnaround times of 0.5 to 1 day for theoretical spectrum generation. These reproducible workflows will be fully integrated with both experimental and data science teams. What You Will Do Deliver spectra at scale: Run quantum chemistry and molecular dynamics calculations to generate reproducible in-silico IR spectra. Pipeline builder: Design, implement, and maintain automated workflows for structure optimization, vibrational analysis, and IR spectrum prediction. QC methods: Apply and benchmark state-of-the-art computational approaches (DFT, molecular dynamics, anharmonic corrections, and related methods) to ensure accuracy and reliability. Infrastructure: Set up and manage the computational environment—HPC, cloud, containers, and schedulers—to enable high-throughput simulations. Automation: Build reproducible pipelines using Python/bash scripting and workflow automation (e.g. ASE, FireWorks, Snakemake, or equivalent). QC integration: Validate computational predictions against experimental IR data, iteratively refining methods and protocols. Collaboration: Work closely with experimental teams to align computational outputs with laboratory results and define standardized molecular ID workflows. Skills and Qualifications PhD in Computational Chemistry, Theoretical Chemistry, or related field. 3–5+ years of hands-on experience with quantum chemistry and molecular dynamics packages (Gaussian, ORCA, CP2K, VASP, GROMACS, or equivalents). Strong track record applying DFT and MD to vibrational spectroscopy or related molecular property prediction . Proven ability to build and run scalable computational pipelines, ideally with HPC or cloud deployment. Solid scripting/programming skills (Python, bash); familiarity with libraries like ASE or MDAnalysis is a plus. Independence and ownership: able to take broad goals and translate them into robust, automated solutions. Clear communicator who can bridge computational and experimental perspectives. Nice To Have Experience automating high-throughput QC calculations. Familiarity with containerization (Docker/Singularity) and HPC/cloud-native workflows. Exposure to cheminformatics tools and spectral databases. Experience in a startup or fast-paced industry environment. Culture and Perks Impact that matters: Help solve one of the most difficult bottlenecks in translational science, trustworthy molecular ID and actionable biomarkers. Build the blueprint: Your systems and standards will define how next-gen metabolomics operates at scale. Elite collaborators: Work with leading hospitals, biotech, and pharma on programs that change patient outcomes. Ownership & growth: High autonomy, rapid learning, and a direct line from your work to patient benefit. #J-18808-Ljbffr
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Lausanne, Schweiz Isospec Analytics VollzeitComputational Chemistry Lead – Molecular Identification At Isospec, we’re building a new standard for molecular identification to unlock patient-centric drug development and diagnostics. You’ll own the computational workflows that turn candidate molecules into high-quality IR spectra—fast, reproducibly, and at scale—enabling direct comparison to...
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