Senior Computational Chemist
| Dyddiad hysbysebu: | 08 Mai 2026 |
|---|---|
| Cyflog: | £42,000 i £46,000 bob blwyddyn, pro rata |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 07 Mehefin 2026 |
| Lleoliad: | Manchester, Greater Manchester |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 5 ddiwrnod yr wythnos |
| Cwmni: | Itec Select |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: |
Crynodeb
Biotechnology | Metals | Molecular Modelling
The Opportunity
An early‑stage biotechnology company is seeking a Senior Computational Chemist to take a strategically important, hands‑on role developing high‑throughput docking and molecular dynamics workflows for metal–peptide and metal–protein systems, including non‑standard and f‑block elements.
This role sits at the intersection of computational chemistry, machine‑learning forcefield development, and experimental decision‑making, with direct influence over pipeline design, experimental prioritisation, and IP generation. You will work closely with a wet‑lab team and senior stakeholders, helping shape a novel computational capability from the ground up.
What You’ll Be Doing
Designing, building, and automating high‑throughput docking pipelines for peptide‑ and protein‑metal binding
Running and optimising molecular dynamics simulations to evaluate binding geometry, stability, and selectivity
Integrating docking, MD, QM, DFT and (over time) QM/MM approaches to define selection rules and go / no‑go criteria
Developing and integrating custom machine‑learning force fields and parameterisation methods for non‑standard metals
Scaling and automating workflows on HPC and GPU environments
Translating modelling outputs into actionable guidance for wet‑lab experiments and programme strategy
Contributing to a fast‑moving, early‑stage technical environment with both scientific and strategic input
Required Experience & Skills
Strong hands‑on experience delivering molecular docking and MD workflows in production or research environments
Demonstrated experience modelling or parameterising metal coordination in biomolecular systems
Exposure to non‑standard metals, including transition metals and/or f‑block elements
Experience with MD engines such as GROMACS, AMBER, OpenMM, NAMD, or CHARMM
Proficiency in Python and/or Bash for scientific automation
Comfortable working on HPC systems, GPUs, and containerised environments
Experience with QM or QM/MM methods (DFT and parameter development expected by end of year one)
Familiarity with enhanced sampling or free energy methods
Experience in early‑stage biotech, startup, or fast‑moving research environments
Prior work developing or implementing machine‑learning force fields or learned potentials
Strong communication skills, including explaining complex modelling outcomes to non‑specialist stakeholders
Desirable (but Not Essential)
Peptide or protein design experience
Development of automated tools or pipelines used by non‑specialists
Background
PhD in Computational Chemistry, Computational Biology, or similar with 3+ years relevant industry experience
or
MSc in a relevant discipline with 5+ years industry experience
Working Pattern
Full time – 32 hours per week, Monday to Friday
Core hours: 09:30–16:00
Designed to support sustainable, accessible working and strong work–life balance.
The Opportunity
An early‑stage biotechnology company is seeking a Senior Computational Chemist to take a strategically important, hands‑on role developing high‑throughput docking and molecular dynamics workflows for metal–peptide and metal–protein systems, including non‑standard and f‑block elements.
This role sits at the intersection of computational chemistry, machine‑learning forcefield development, and experimental decision‑making, with direct influence over pipeline design, experimental prioritisation, and IP generation. You will work closely with a wet‑lab team and senior stakeholders, helping shape a novel computational capability from the ground up.
What You’ll Be Doing
Designing, building, and automating high‑throughput docking pipelines for peptide‑ and protein‑metal binding
Running and optimising molecular dynamics simulations to evaluate binding geometry, stability, and selectivity
Integrating docking, MD, QM, DFT and (over time) QM/MM approaches to define selection rules and go / no‑go criteria
Developing and integrating custom machine‑learning force fields and parameterisation methods for non‑standard metals
Scaling and automating workflows on HPC and GPU environments
Translating modelling outputs into actionable guidance for wet‑lab experiments and programme strategy
Contributing to a fast‑moving, early‑stage technical environment with both scientific and strategic input
Required Experience & Skills
Strong hands‑on experience delivering molecular docking and MD workflows in production or research environments
Demonstrated experience modelling or parameterising metal coordination in biomolecular systems
Exposure to non‑standard metals, including transition metals and/or f‑block elements
Experience with MD engines such as GROMACS, AMBER, OpenMM, NAMD, or CHARMM
Proficiency in Python and/or Bash for scientific automation
Comfortable working on HPC systems, GPUs, and containerised environments
Experience with QM or QM/MM methods (DFT and parameter development expected by end of year one)
Familiarity with enhanced sampling or free energy methods
Experience in early‑stage biotech, startup, or fast‑moving research environments
Prior work developing or implementing machine‑learning force fields or learned potentials
Strong communication skills, including explaining complex modelling outcomes to non‑specialist stakeholders
Desirable (but Not Essential)
Peptide or protein design experience
Development of automated tools or pipelines used by non‑specialists
Background
PhD in Computational Chemistry, Computational Biology, or similar with 3+ years relevant industry experience
or
MSc in a relevant discipline with 5+ years industry experience
Working Pattern
Full time – 32 hours per week, Monday to Friday
Core hours: 09:30–16:00
Designed to support sustainable, accessible working and strong work–life balance.