Introduction
This trainee direction focuses on tensor network or other computational physics methods and their applications in scientific computing, quantum many-body simulation, and machine-learning-related numerical modeling.
Available project directions by May 2nd include:
- Matrix Product States (MPS), DMRG, PEPS-inspired methods
- Tensor decomposition algorithms
- Tensor-network-based representations for high-dimensional physical systems or machine learning systems
- Observable computations and physics validation
- Hybrid Quantum Classical Algorithms
- Introductory solid state physics computations
- Introductory optical computations
The training may include implementing tensor network algorithms, building reproducible experimental code, designing benchmarks, comparing tensor network methods with neural network methods, and analyzing how bond dimension, ordering, truncation strategy, and decomposition choices affect numerical accuracy and computational efficiency.
This position supports mentor mode.Note that this program is not designed for profit, all revenues are used to support lab expenses.
Trainee Model
This program mainly provides Hybrid or remote opportunities.
This program also provides onsite (Berkeley, CA) based on availability. Onsite seats is very limited for this program but requests will still be considered if there is any.
This program is a paid mentorship-based research training format. You will work with 1ā2 mentors on a selected research topic, typically in a small team (1ā4 teammates).
The program includes:
ā Up to five 1-hour mentorship sessions (1-3 trainees)
ā Ongoing guidance and topic discussion as needed
ā Support in developing qualifications and refining the project
The mentorship fee reflects the contracted time and effort of our mentors.
For any research / project outputs, authorship will be determined based on actual contribution following standard academic practices.
Intellectual property (IP) and any potential revenue distribution, if applicable, will be handled according to project-specific agreements and applicable regulations.
Note that some teams might require quantum mechanics or statistical physics as prerequisites, extra sessions (2-3 hours) usually cost from $200 to max.$450 ($100-$150/hour). This follows each team's trainee rubrics and is not included in the regular trainee contract.
Mentorship: $800 (Single Mentor); $1200 (Interdisciplinary Co-mentor)
Preferred Alignments
Academic background in mathematics, physics, EE, computer science, statistics, or related fields.
Or biology and chemistry majors interested in mechanisms and computational representations and simulations in the physics scale, with some prior exposure to one of linear algebra, numerical methods, discrete math, quantum physics and computational coding (MatLab, C++, Julia, Python).
Nice-to-have
Experience with DMRG, MPS, PEPS, tensor decomposition, or other tensor network methods.
Background in quantum mechanics, statistical mechanics, condensed matter physics, quantum information, or lattice models.
Experience with numerical simulation, Hamiltonian systems, spin chains, variational methods, or exact diagonalization.
Experience with high-performance computing, GPU acceleration, CUDA, or parallel computation.
Why Us
Computation group plays the core role of Forecaster AI, we provide support to theoretical natural science research platform, develop novel computational approaches for quantum science, artificial intelligence and are gradually applying to physics, chemistry, finance and so on. We initiated the philosophy of focus micro, FORECAST MACRO, and keep practicing public awareness, openness, and respect.
We work rigorously, dream unlimited, and welcome peers to join.
We can consider housing coverage and OPT/H1B sponsorship based on availability.