Data-intensive PhDs at LIV.INNO prepare students for careers outside of academia
Oct 17, 2024
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Carsten Welsch, an accelerator physicist and director of LIV.INNO, and Andreea Font, a computational astrophysicist, dive into the unique PhD training offered at LIV.INNO. They discuss the importance of equipping students with skills in high-performance computing and machine learning for diverse careers beyond academia. The duo highlights the significance of industry placements and interdisciplinary collaborations, especially in applying data science to fields like healthcare and agriculture. They also touch upon the evolving role of AI in education, emphasizing the need for critical analysis of information.
LIV.INNO equips PhD students with skills in high-performance computing and machine learning to prepare them for industry careers.
Mandatory industry placements provide vital real-world experience, enhancing students' research skills and facilitating smoother career transitions.
Deep dives
The Purpose and Value of a PhD
The primary purpose of a PhD is to demonstrate the ability to conduct independent research, which encompasses significant skill development beyond basic research techniques. A successful PhD program should prepare students for multiple career pathways, particularly highlighting the importance of adapting training to meet the evolving landscape of job markets, especially as many PhD graduates move into industry roles. The ideal academic environment fosters creativity and collaboration, and also provides access to substantial research infrastructure where students can engage in large-scale experiments. Such an approach ensures that PhD programs not only contribute to academic knowledge but also prepare graduates to contribute meaningfully to society.
The Impact of Data-Intensive Research in Astrophysics
Astrophysics relies heavily on vast amounts of data for analysis, particularly as new missions like the Euclid spacecraft are set to generate substantial data daily over extended periods. Machine learning plays a critical role in managing this data, enabling researchers to classify and analyze immense datasets that would be overwhelming for humans alone. This tool facilitates the identification of various celestial phenomena, helping astronomers make efficient classifications of galaxies, asteroids, and supernovae. By integrating machine learning into astrophysical research, the field can accelerate discoveries and remain at the forefront of scientific advancement.
Adapting PhD Training to Industry Needs
The physics and astrophysics communities are increasingly adapting to the requirements of data-intensive research, emphasizing the importance of equipping PhD students with the necessary skills for careers outside academia. As research techniques evolve rapidly, programs have started incorporating hands-on training in new methodologies, balancing traditional academic training with modern skills such as data literacy and entrepreneurship. Students gain from transformative experiences through industry placements, which not only provide practical application of their research skills but also offer them valuable insights into real-world problem-solving. These initiatives enhance the overall educational experience and prepare students for diverse career pathways in various sectors.
Successful Synergy Between Academia and Industry
PhD students' mandatory industry placements serve as a vital link between academia and real-world applications, benefiting both students and their supervisors. These experiences allow students to apply their academic skills to industry problems, fostering a return to their research with renewed energy and innovative ideas. The collaborations have proven fruitful, as supervisors gain new industry connections and students often receive job offers from companies where they interned, facilitating smoother transitions to their careers. Such partnerships illustrate the value of integrating academic training with industry experience, which enriches both educational frameworks and commercial endeavors.
LIV.INNO, Liverpool Centre for Doctoral Training for Innovation in Data-Intensive Science, offers students fully-funded PhD studentships across a broad range of research projects from medical physics to quantum computing. All students receive training in high-performance computing, data analysis, and machine learning and artificial intelligence. Students also receive career advice and training in project management, entrepreneurship and communication skills – preparing them for careers outside of academia.
This podcast features the accelerator physicist Carsten Welsch, who is head of the Accelerator Science Cluster at the University of Liverpool and director of LIV.INNO, and the computational astrophysicist Andreea Font who is a deputy director of LIV.INNO.
They chat with Physics World’s Katherine Skipper about how LIV.INNO provides its students with a wide range of skills and experiences – including a six-month industrial placement.
This podcast is sponsored by LIV.INNO, the Liverpool Centre for Doctoral Training for Innovation in Data-Intensive Science.
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