Company Overview:

Axoft is committed to developing implantable devices that enable ultrasoft and scalable brain-computer interfaces. Our mission is to facilitate long-term stable communication between external world and the patient’s brain, with ultrasoft materials that minimize the immune response, minimal invasiveness implantation technique, ultra-high bandwidth that allows minimal latency and enhanced accuracy. Our aim is to provide new therapies and treatments for neurological disorders and physical disabilities.

Our team is passionate about improving patient outcomes and we believe our innovative technology has the potential to advance the field of brain-computer interface technology. If you are interested in being part of a team that is committed to improving the lives of patients and pushing the boundaries of medical technology, then Axoft could be the perfect place for you to work.

Job Overview

Axoft is currently seeking a highly motivated software engineering intern to join our team either on a full-time basis. In this role, you will contribute to the development of software solutions for ultrasoft brain probes designed for large animals and potentially human patients. It is important to note that these brain probes are exceptionally soft, resembling gel-like material, and thinner than a human hair, making them highly unique in the field.

We invite qualified candidates to apply for this position and join us in our mission to revolutionize the field of brain-computer interfaces, uncover new insights into brain function and disease, and make a significant contribution to the advancement of science and technology.

As a Software Engineering Intern at Axoft, you will have the extraordinary opportunity to work with one of the softest brain probe in the world, fabricated using cutting-edge technology. You will collaborate closely with the engineers and researchers from diverse background, under the guidance of experienced senior engineers, to design and build a functional prototype of future brain-computer interface system. This is an on-site position located in the Cambridge, Massachusetts area, and can be undertaken either on a full-time or part-time basis. Please note that at least part-time availability during the semester for several months is required for this position.

Responsibilities/Tasks:

· Collaborate with various teams at Axoft to understand and analyze their needs, develop software tools that streamline processes as well as enhance workflows.

· Design robust software solutions with advanced graphical user interfaces (GUIs), develop web dashboards, and create plugins for existing tools.

· Assist in supporting the data processing pipeline and scaling workflow pipelines in collaboration with scientists and researchers.

· Create comprehensive documentation for different software projects and regularly report progress to contribute to software strategy improvements.

Qualifications:

· BSc in Computer Science or related fields.

· Strong interest in neuroscience.

· 3+ years of Python programming experience. Proficiency with various libraries, such as PyQt, OpenCV, Flask, Dash, Plotly, Pytest, Tensorflow, and Pytorch.

· Proven theoretical background in digital signal processing (Advanced Statistics and Linear Algebra, Fourier Transform, Wavelet Transform, Digital Filter Design...). Proficient with the implementation of DSP methods in Python (Scipy, Numpy)[JL1]

· Familiarity with Git/GitHub, related CI/CD tools, unit testing and Agile project management

· Experience with responsive web and desktop design using Bootstrap [JL2] or QtWidgets.

· Basic knowledge of C++, CUDA, Bash and Groovy.

· Experience working with Linux servers and familiarity with tools and services like Docker, Singularity, Samba, Wireguard, OneDrive, and Office.

• Strong problem-solving skills and a proactive mindset.

Preferred qualifications:

· MSc/PhD in Computer Science or related fields

· Knowledge of spike sorting techniques (motion correction, clustering, template matching) and proficiency with Python libraries like spikeinterface, pynwb.

· Experience with Signal Processing for Electrophysiological Signals, Machine-Learning for Timeseries

· Experience with DSM 7.1