Laura Moschini

Dr. Laura Moschini is a Research Fellow in Theoretical Nuclear Physics at the School of Mathematics and Physics, University of Surrey. She earned her PhD jointly from Università degli Studi di Padova (Italy) and Universidad de Sevilla (Spain) in 2017, followed by postdoctoral research at Université libre de Bruxelles under Horizon 2020 funding. Since 2020, she has been at the University of Surrey, supported initially by an STFC grant and currently by a Leverhulme Trust project.

Her research focuses on quantum dynamics in nuclear reactions, particularly the interplay of processes in exotic nuclei and the effects of external environments such as laser fields. She develops theoretical models to describe reaction mechanisms in low-energy nuclear fusion, breakup, and transfer processes, with applications in element synthesis and astrophysics.

Her recent publications in journals like Physical Review C and Physics Letters B highlight trends in laser-assisted fusion, the role of three-nucleon forces, and the use of Halo Effective Field Theory to probe nuclear structure. These works reflect a strong emphasis on bridging nuclear structure and reaction theory, with implications for both fundamental science and energy research.

• Research Fellow, University of Surrey (2020–present)
• Postdoctoral Researcher, Université libre de Bruxelles (2017–2020)
• PhD in Theoretical Nuclear Physics, Università degli Studi di Padova & Universidad de Sevilla (2017)

Dr. Moschini is actively involved in mentoring, currently co-supervising Nicholas Thomson on laser-assisted nuclear reaction studies. Her research is funded by the Leverhulme Trust, continuing her prior STFC-supported work. She collaborates widely with international institutions including INFN, LNL, and RIKEN.

She is a member of major collaborative efforts, including the SPES facility development at Legnaro National Laboratories, contributing to the strategic vision of nuclear physics in Europe. Her work integrates advanced quantum dynamical models with experimental data to refine theoretical predictions.