  The successful realization of the EIC scientific program requires the design
and construction of high-performance particle detectors. Recent developments in
the field of scientific computing and increased availability of high
performance computing resources have made it possible to perform optimization
of multi-parameter designs, even when the latter require longer computational
times (for example simulations of particle interactions with matter).
Procedures involving machine-assisted techniques used to inform the design
decision have seen a considerable growth in popularity among the EIC detector
community. Having already been realized for tracking and RICH PID detectors, it
has a potential application in calorimetry designs. A SciGlass barrel
calorimeter originally designed for EIC Detector-1 has a semi-projective
geometry that allows for non-trivial performance gains, but also poses special
challenges in the way of effective exploration of the design space while
satisfying the available space and the cell dimension constraints together with
the full detector acceptance requirement. This talk will cover specific
approaches taken to perform this detector design optimization.
