PhD Studentship: In Silico Directed Evolution of Biological Qubits: Rational Design of Nanoscale Quantum Transducers

University of Exeter; Exeter; On-site; GBP 20,000 - 24,000; Full time

Job summary

The University of Exeter invites applications for a funded PhD studentship that aims to engineer biological qubits converting spin signals into cellular responses. The project fuses open quantum spin dynamics with deep-learning protein design to form an in silico directed-evolution pipeline, supervised by LSI and NPL experts.

The scholar will use ColabDesign, ProteinMPNN and DFT-based methods to explore new protein scaffolds, enabling nanoscale quantum sensing inside living cells.

Qualifications

• Experience with open quantum spin dynamics and Bayesian uncertainty.
• Familiarity with de novo protein design and AI-based sequence generation.
• Strong programming skills in Python and MD/simulation workflows.

Responsibilities

• Develop an in silico directed evolution pipeline for biological qubits.
• Integrate multiscale simulations with AI-powered protein design tools.
• Collaborate with LSI and NPL teams for experimental direction.

Skills

Open quantum spin dynamics

Generative AI design

Molecular dynamics

Bayesian uncertainty

Education

Pursuing PhD in biophysics/chemistry/computational biology

Tools

ColabDesign

ProteinMPNN

DFT

Job description

Engineered flavoproteins, LOV domains, and fluorescent proteins have recently demonstrated that radical pair mechanisms (RPM) can serve as biological qubits with optical readouts, achieving magnetic field responses up to a 50% fluorescence change in living cells. However, reaching such a large magnetic sensitivity involved empiric and slow laboratory exploration, including directed evolution, and the (optimized) function is strictly that of a passive quantum observers. The scientific frontier of this project lies in the transition from passive quantum observers (fluorescence-based readouts) to active quantum transducers, i.e., genetically encoded systems that actuate a downstream biological response directly governed by quantum spin dynamics. Concurrently, rapid breakthroughs in generative AI and de novo protein design enable the systematic exploration of sequence and backbone space beyond natural scaffolds.

By unifying open quantum system spin dynamics simulations with deep-learning protein generation, this project will establish an in silico directed evolution pipeline to engineer biological qubits that transduce spin-dependent events into functional, biological effector signals.

Project Aims and Objectives

• Aim 1: Mapping Optimal Spin Dynamics to Structural Targets. Translate theoretically optimal spin Hamiltonians (maximizing magnetic field sensitivity via tuned hyperfine couplings, exchange interactions, and recombination kinetics) into concrete physical constraints, such as inter-cofactor geometries and local environments.
• Aim 2: In Silico Generative Protein Design. Develop a de novo design workflow leveraging deep-learning inverse-folding algorithms and electronic structure estimations to generate novel protein scaffolds and sidechain packings optimized for radical pair generation and (coherent) lifetime.
• Aim 3: Pipeline Calibration & Benchmarking. Calibrate the computational pipeline by reverse engineering existing laboratory directed-evolution constructs (such as MagLOV variants).
• Aim 4: Engineering Biological Transduction. Design allosteric coupling pathways wherein spin-selective chemical yields or radical lifetimes trigger structural shifts, converting the quantum sensor into a biological affector (e.g., modulating protein-protein interactions or enzymatic activity). Prospective predictions will be considered for experimental validation via Engineering and Physical Sciences Research Council Doctoral Landscape Award collaborating facilities at the Living Systems Institute (LSI) and National Physical Laboratory (NPL).

Methodology

The candidate will implement a multiscale computational framework combining open quantum spin dynamics (utilizing stochastic Liouville solvers and accounting for parametric uncertainty using a Bayesian approach) with generative AI protein design tools (e.g., ColabDesign, ProteinMPNN), molecular dynamics free-energy calculations, and ab initio prediction of spin Hamiltonian parameters and electron transfer rates (DFT based). The project is co-supervised by Prof. Alex Jones at NPL in support of the Laboratory’s strategic initiative in nanoscale quantum sensors for intracellular measurement and will leverage protein design methodology of start-up Orion Life Gmbh.

Funded by: EPSRC Engineering and Physical Sciences Research Council

• £21,805. The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.