美國加州大學(xué)默賽德分校2023年招聘博士后職位（應(yīng)用數(shù)學(xué)）

加利福尼亞大學(xué)（University of California），簡稱加州大學(xué)（UC），是美國加利福尼亞州擁有十個校區(qū)的大學(xué)系統(tǒng)，是世界上最具影響力的公立大學(xué)系統(tǒng)之一，被譽為“公立高等教育的典范”。

加州大學(xué)起源于1853年建立在奧克蘭的加利福尼亞學(xué)院（College of California），1868年3月正式更名為“加州大學(xué)”。1873年，學(xué)校遷入新址，為了紀念18世紀最偉大的哲學(xué)家之一喬治·貝克萊，新的大學(xué)城被命名為“伯克利市”，此后逐漸在洛杉磯等地開設(shè)分校區(qū)。1952年起，“加州大學(xué)”作為一個行政系統(tǒng)逐漸與伯克利加州大學(xué)分離。與此同時，加州各地的分校區(qū)也逐漸升格為與伯克利平級的大學(xué)。

Postdoctoral Scholar in Applied Mathematics

University of California, Merced

Position overview

Position title: Postdoctoral Scholar

Salary range: See Table 23 for the salary range for this position. A reasonable estimate for this position is $60,000 - $ 71,952.

Percent time: 100%

Anticipated start: January 16, 2024

Position duration: 2 years

Application Window

Open date: August 24, 2023

Next review date: Wednesday, Nov 1, 2023 at 11:59pm (Pacific Time) Apply by this date to ensure full consideration by the committee.

Final date: Friday, Dec 15, 2023 at 11:59pm (Pacific Time) Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

Position description

Prof. Harish S. Bhat (https:// faculty.ucmerced.edu/hbhat/) in Applied Mathematics at UC Merced is seeking applicants for a postdoctoral position focused on automated learning of reduced-order models for quantum dynamics. The postdoctoral scholar will work on new mathematical and computational methods to learn tractable models that accurately predict the dynamics of time-dependent quantum systems. Systems/problems of interest include electron dynamics, nuclear/spin dynamics, and optimizing coherence times for quantum circuits.

The postdoctoral scholar will contribute to methods that fuse first-principle modeling (including numerical analysis and scientific computing) with machine learning modeling of quantum Hamiltonian terms (e.g., using neural networks). To train these models, the postdoctoral scholar will develop and apply optimization methods that are constrained by time-dependent, physical dynamics with symmetries and invariants. The postdoctoral scholar will be free to develop and incorporate ideas from a diverse array of subfields including physics-constrained learning, equation discovery, dimensionality reduction, interpretable machine learning, geometric mechanics, and/or optimal control. The resulting methods will enable simulation and control of quantum systems that cannot be handled by currently available methods.

The postdoctoral scholar will work closely with Prof. Bhat and will also be co-mentored by Prof. Christine Isborn (Chemistry, UC Merced). The postdoctoral scholar will be expected to publish in peer-reviewed journals and proceedings, to develop and publish open-source software, to present research findings at conferences, and to work with graduate and undergraduate student researchers.

Qualifications

Basic qualifications

A PhD in Applied Mathematics, Control & Dynamical Systems, Theoretical/Computational Physics, Theoretical/Computational Chemistry, or a related field

Experience developing new computational methods and implementing these methods in code, e.g., with Python and NumPy/SciPy

Interest in machine learning and quantum dynamics

Ability to effectively communicate verbally and in writing

Additional qualifications

Experience publishing peer reviewed articles and presenting at technical conferences

Preferred qualifications

Experience with one or more of the following areas: PDE-constrained optimization, optimal control, numerical simulation of quantum systems, physics-constrained learning, and/or geometric mechanics

Proficiency in scientific computing on modern clusters with GPU nodes

Experience with machine learning frameworks such as JAX, PyTorch, and/or TensorFlow

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