Application form

Full name

Email

Select up to 5 projects in order of preference (1st = most preferred). At least one preference is required.

Preference 1 * — not selected — DC1 — Exascale-enabled scattering codes and pion-nucleon scattering DC2 — Next-generation CPU and accelerator technologies for exploring the sea quark content of the nucleon DC3 — Efficient multigrid solvers for multiple Krylov inversions to compute electromagnetic corrections in neutron $\beta$-decay DC4 — Calculation of parton distribution functions via Mellin moments and Machine Learning-improved Wilson flow DC5 — Quantum inspired methods for out-of-equilibrium dynamics in high dimensional Lattice Gauge Theories DC6 — Turbulence super-resolution, based on combining deep learning and turbulent statistics conditioning DC7 — Lagrangian particle spatial distribution on coarse-grained flow fields: a Machine Learning approach DC8 — Mechanistic Interpretability for data-driven models in fluid dynamics DC9 — Integrating Machine Learning and biophysical simulations for accurate free energy predictions DC10 — Integration of simulations and Machine Learning algorithms: accurate descriptors from enhanced conformational sampling DC11 — Tensor networks for biomolecular configuration space analysis DC12 — Tensor-network-based Ansätze and hardware-aware compilation methods for ground-state preparation for quantum chemistry

Preference 2 — not selected — DC1 — Exascale-enabled scattering codes and pion-nucleon scattering DC2 — Next-generation CPU and accelerator technologies for exploring the sea quark content of the nucleon DC3 — Efficient multigrid solvers for multiple Krylov inversions to compute electromagnetic corrections in neutron $\beta$-decay DC4 — Calculation of parton distribution functions via Mellin moments and Machine Learning-improved Wilson flow DC5 — Quantum inspired methods for out-of-equilibrium dynamics in high dimensional Lattice Gauge Theories DC6 — Turbulence super-resolution, based on combining deep learning and turbulent statistics conditioning DC7 — Lagrangian particle spatial distribution on coarse-grained flow fields: a Machine Learning approach DC8 — Mechanistic Interpretability for data-driven models in fluid dynamics DC9 — Integrating Machine Learning and biophysical simulations for accurate free energy predictions DC10 — Integration of simulations and Machine Learning algorithms: accurate descriptors from enhanced conformational sampling DC11 — Tensor networks for biomolecular configuration space analysis DC12 — Tensor-network-based Ansätze and hardware-aware compilation methods for ground-state preparation for quantum chemistry

Preference 3 — not selected — DC1 — Exascale-enabled scattering codes and pion-nucleon scattering DC2 — Next-generation CPU and accelerator technologies for exploring the sea quark content of the nucleon DC3 — Efficient multigrid solvers for multiple Krylov inversions to compute electromagnetic corrections in neutron $\beta$-decay DC4 — Calculation of parton distribution functions via Mellin moments and Machine Learning-improved Wilson flow DC5 — Quantum inspired methods for out-of-equilibrium dynamics in high dimensional Lattice Gauge Theories DC6 — Turbulence super-resolution, based on combining deep learning and turbulent statistics conditioning DC7 — Lagrangian particle spatial distribution on coarse-grained flow fields: a Machine Learning approach DC8 — Mechanistic Interpretability for data-driven models in fluid dynamics DC9 — Integrating Machine Learning and biophysical simulations for accurate free energy predictions DC10 — Integration of simulations and Machine Learning algorithms: accurate descriptors from enhanced conformational sampling DC11 — Tensor networks for biomolecular configuration space analysis DC12 — Tensor-network-based Ansätze and hardware-aware compilation methods for ground-state preparation for quantum chemistry

Preference 4 — not selected — DC1 — Exascale-enabled scattering codes and pion-nucleon scattering DC2 — Next-generation CPU and accelerator technologies for exploring the sea quark content of the nucleon DC3 — Efficient multigrid solvers for multiple Krylov inversions to compute electromagnetic corrections in neutron $\beta$-decay DC4 — Calculation of parton distribution functions via Mellin moments and Machine Learning-improved Wilson flow DC5 — Quantum inspired methods for out-of-equilibrium dynamics in high dimensional Lattice Gauge Theories DC6 — Turbulence super-resolution, based on combining deep learning and turbulent statistics conditioning DC7 — Lagrangian particle spatial distribution on coarse-grained flow fields: a Machine Learning approach DC8 — Mechanistic Interpretability for data-driven models in fluid dynamics DC9 — Integrating Machine Learning and biophysical simulations for accurate free energy predictions DC10 — Integration of simulations and Machine Learning algorithms: accurate descriptors from enhanced conformational sampling DC11 — Tensor networks for biomolecular configuration space analysis DC12 — Tensor-network-based Ansätze and hardware-aware compilation methods for ground-state preparation for quantum chemistry

Preference 5 — not selected — DC1 — Exascale-enabled scattering codes and pion-nucleon scattering DC2 — Next-generation CPU and accelerator technologies for exploring the sea quark content of the nucleon DC3 — Efficient multigrid solvers for multiple Krylov inversions to compute electromagnetic corrections in neutron $\beta$-decay DC4 — Calculation of parton distribution functions via Mellin moments and Machine Learning-improved Wilson flow DC5 — Quantum inspired methods for out-of-equilibrium dynamics in high dimensional Lattice Gauge Theories DC6 — Turbulence super-resolution, based on combining deep learning and turbulent statistics conditioning DC7 — Lagrangian particle spatial distribution on coarse-grained flow fields: a Machine Learning approach DC8 — Mechanistic Interpretability for data-driven models in fluid dynamics DC9 — Integrating Machine Learning and biophysical simulations for accurate free energy predictions DC10 — Integration of simulations and Machine Learning algorithms: accurate descriptors from enhanced conformational sampling DC11 — Tensor networks for biomolecular configuration space analysis DC12 — Tensor-network-based Ansätze and hardware-aware compilation methods for ground-state preparation for quantum chemistry

All files must be PDF, maximum 30 MB per file. Under "Transcript(s)", please attach all tertiary level degrees and transcripts that prove your eligibility for a PhD, i.e. BSc and MSc transcripts, or equivalent.

CV*

Transcript(s)* You can select more than one files. In the file picker, hold Cmd (Mac) or Ctrl (Linux and Windows) and click each file to attach more than one.

Cover letter / statement of motivation*

Other (optional) You can select more than one files. In the file picker, hold Cmd (Mac) or Ctrl (Linux and Windows) and click each file to attach more than one.

Provide the names and email addresses of 3 referees that will be contacted to provide a recommendation letter in support of your application. The deadline for receiving reference letters is: 15 December 2026.

By submitting this form, you confirm that your referees have agreed to provide a reference and to be contacted at the addresses above. We'll email each referee a secure link to upload their letter, sharing your name and the project(s) you applied to.

Please make sure they know to expect an email from noreply@elevate-ejd.eu (it may otherwise be filtered as spam).

How we handle your and your referees' data is explained in our privacy notice.